These days, it seems like enterprises everywhere are striving to be data-driven. According to Forrester, 74 percent of firms say they want to be data-driven, but only 29 percent are managing to achieve it.
For legacy enterprises competing with modern, agile businesses like Uber and Amazon, the transition to a data-driven business poses many challenges that reach deep into how the organization functions.
These organizational-level problems are critical to success so WebbMason Analytics studies a client’s talent and skills base, workflows and business processes to identify areas where the organization needs to make adjustments.
Analytics is a team sport that requires a holistic approach
Typical Analytics Group Line up:
- Business stakeholders and product owners
- Cloud operations engineers
- Data and analytic consultants
- Data engineers
- Data governance specialists
- Data scientists
- Platform engineers
- Scrum masters
- Solution architects
Advanced analytics does not exist inside silos. It requires the cooperation of multiple departments and people across a company. From the stakeholders who have ultimate responsibility for making insight-driven decisions to the data science team that builds the models to data engineers, cloud architects and others within the enterprise, everyone needs to work together.
But, in large organizations, these disparate groups operate in relative isolation. They have distinctive hierarchies, goals and procedures. Finding a way for everyone to work seamlessly together is not something that happens by osmosis.
We have identified three ways for organizations to address this:
1. Alignment of skills and technology
There are a lot of factors that go into choosing the right technologies for your analytic platform. The human aspect is frequently overlooked – the skills of your workforce and how distributed teams function.
If you select technologies that are not aligned with user skills, it is going to be an uphill struggle to get value out of the system. The kind of technologies we would specify for a team with deep engineering skills is going to be very different to the selection we would offer less technical users who need the support of a graphical user interface (GUI) editor.
In addition, thought should be given to making sure that any technology selected can be configured to work in a way that complements existing development and deployment processes within your organization. Each technology has its own built-in processes designed to move a solution from development to action. It is important to incorporate those steps into your team’s workflows so that each can be accomplished efficiently
2. Getting granular about responsibilities
Collaboration between groups is a critical component of analytics success, and while it might sound easy, it is not something that happens organically. Instead, you need to deliberately integrate it into team workflows through role and responsibility assignments. Just directing people to work together will not make it happen. You have to develop guidelines and processes so people know how they are expected to interact.
Ensuring that people and departments are aware of what they own and are responsible for enables individual contributors to collaborate. With a clear understanding of their roles and responsibilities, they know where they fit and how they can make an impact in the analytic organization3.
3. Clear job descriptions and seniority levels
Resourcing projects with people who have the right skills is critical to both efficient delivery and collaboration. But the truth is, many companies do not know the specific skills or seniority levels they have on their analytic teams. It can be difficult to tell who is a good Python developer and who is a great Python developer. Adding to the complexity is the sheer number of technologies within organizations today. Providing clear job descriptions and seniority levels is one of the most effective ways to resolve this challenge.
Clear job descriptions help management create groups with the right combination of skills to execute all its projects. They give you a picture of a team’s strengths and weaknesses, reveal talent gaps that you can fill through training or with new hires and they support better resource planning. Resourcing projects with people who have the right skills and seniority levels will aid efficiency and expose natural collaboration points.
Supporting the end-to-end process
When you create a cultural environment where everyone understands how each player on the team supports other players, you make it possible for people to work together towards a common goal. Creating this environment requires aligning skills and technologies, defining granular responsibilities, and developing clear job descriptions and seniority levels. Addressing these three areas supports clear ways of working, increasing the odds you can see an analytic solution from development to activation.